Is it fair to compare AlphaGo with a Human player?
Is it fair to compare AlphaGo with a Human player?


Depends on the purpose of the comparison.

If we are comparing ability to win a game of Go, then yes.

If we are comparing learning ability, then maybe. It depends on the task. AlphaGo and systems like it are capable of learning only in well-described limited domains. There may be an analogy with sensory learning (it might even be possible in theory to take a small piece of brain tissue and run an algorithm similar to AlphaGo's learning process on it). 

In general, the approach used by AlphaGo and other reinforcement learning successes is "trial-and-error plus function approximation". It seems analogous to perception and motor skills, such as object recognition or riding a bike, as opposed to reasoning skills and games as humans play them, which goes through many more cognitive and conscious layers that have no real analog in a RL system like AlphaGo.


  A human player plays limited games compared to a system that undergoes millions of iterations


This is an advantage of a machine to learn this kind of task. It would equally apply in other simulated environments with simple rules. If your goal is to have the most skilled and optimal navigation of such a domain, the implication now is that you would not train a human expert through years of study, but to write the simulator and train an AlphaGo-like machine. 

This is no different a comparison than deciding cars and roads are better solutions to long distance travel for the general population than walking or horses and carts. It doesn't matter what underlies the advantage of one over the other, the assessment is cost/benefit, which resolves to a single comparable number. 

It would, however, be wrong to assess AlphaGo as a better general-purpose learning engine than a human. The fact that humans do not have to work fully through millions of simulations in full detail is important. It means that something about how humans learn is still not covered by learning machines. Some of these things are understood and being discussed - such as the ability to focus intuitively on important aspects of what to learn, the ability to reason about the environment, learning analogously or transfer learning from other domains.